Overview

Dataset statistics

Number of variables8
Number of observations552
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.6 KiB
Average record size in memory64.2 B

Variable types

DateTime1
Numeric7

Dataset

DescriptionReports of cleaned electricity dataset
URL

Variable descriptions

Average Retail Price of Electricity, ResidentialPrice paid by residential homes
Average Retail Price of Electricity, CommercialPrice paid by commercial entities
Average Retail Price of Electricity, IndustrialPrice paid by industrial companies
Average Retail Price of Electricity, TransportationPrice paid by electrical transportation users
Average Retail Price of Electricity, TotalCumulative mean of all categories

Alerts

Average Retail Price of Electricity, Residential is highly correlated with Average Retail Price of Electricity, Commercial and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Commercial is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Industrial is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Transportation is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Total is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Residential is highly correlated with Average Retail Price of Electricity, Commercial and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Commercial is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Industrial is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Transportation is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Total is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Residential is highly correlated with Average Retail Price of Electricity, Commercial and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Commercial is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Industrial is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Transportation is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Average Retail Price of Electricity, Total is highly correlated with Average Retail Price of Electricity, Residential and 3 other fieldsHigh correlation
Year is highly correlated with Average Retail Price of Electricity, Residential and 4 other fieldsHigh correlation
Month is highly correlated with Average Retail Price of Electricity, TransportationHigh correlation
Average Retail Price of Electricity, Residential is highly correlated with Year and 4 other fieldsHigh correlation
Average Retail Price of Electricity, Commercial is highly correlated with Year and 4 other fieldsHigh correlation
Average Retail Price of Electricity, Industrial is highly correlated with Year and 4 other fieldsHigh correlation
Average Retail Price of Electricity, Transportation is highly correlated with Year and 5 other fieldsHigh correlation
Average Retail Price of Electricity, Total is highly correlated with Year and 4 other fieldsHigh correlation
Date has unique values Unique

Reproduction

Analysis started2022-08-09 18:53:02.315149
Analysis finished2022-08-09 18:53:11.278728
Duration8.96 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct552
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1976-01-01 00:00:00
Maximum2021-12-01 00:00:00
2022-08-09T11:53:11.345731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:11.495851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.5
Minimum1976
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:11.666861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1978
Q11987
median1998.5
Q32010
95-th percentile2019
Maximum2021
Range45
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.2879597
Coefficient of variation (CV)0.006648966574
Kurtosis-1.201137149
Mean1998.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1103172
Variance176.569873
MonotonicityIncreasing
2022-08-09T11:53:11.812864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
197612
 
2.2%
201012
 
2.2%
200112
 
2.2%
200212
 
2.2%
200312
 
2.2%
200412
 
2.2%
200512
 
2.2%
200612
 
2.2%
200712
 
2.2%
200812
 
2.2%
Other values (36)432
78.3%
ValueCountFrequency (%)
197612
2.2%
197712
2.2%
197812
2.2%
197912
2.2%
198012
2.2%
198112
2.2%
198212
2.2%
198312
2.2%
198412
2.2%
198512
2.2%
ValueCountFrequency (%)
202112
2.2%
202012
2.2%
201912
2.2%
201812
2.2%
201712
2.2%
201612
2.2%
201512
2.2%
201412
2.2%
201312
2.2%
201212
2.2%

Month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:11.942935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.455183644
Coefficient of variation (CV)0.5315667144
Kurtosis-1.216928288
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum3588
Variance11.93829401
MonotonicityNot monotonic
2022-08-09T11:53:12.040947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
146
8.3%
246
8.3%
346
8.3%
446
8.3%
546
8.3%
646
8.3%
746
8.3%
846
8.3%
946
8.3%
1046
8.3%
Other values (2)92
16.7%
ValueCountFrequency (%)
146
8.3%
246
8.3%
346
8.3%
446
8.3%
546
8.3%
646
8.3%
746
8.3%
846
8.3%
946
8.3%
1046
8.3%
ValueCountFrequency (%)
1246
8.3%
1146
8.3%
1046
8.3%
946
8.3%
846
8.3%
746
8.3%
646
8.3%
546
8.3%
446
8.3%
346
8.3%

Average Retail Price of Electricity, Residential
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Price paid by residential homes

Distinct385
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.059483905
Minimum3.6
Maximum14.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:12.169943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile4.3
Q17.584264763
median8.49
Q311.655
95-th percentile13.139
Maximum14.19
Range10.59
Interquartile range (IQR)4.070735237

Descriptive statistics

Standard deviation2.634225294
Coefficient of variation (CV)0.2907699072
Kurtosis-0.7589440461
Mean9.059483905
Median Absolute Deviation (MAD)1.507776691
Skewness0.02636916558
Sum5000.835115
Variance6.939142899
MonotonicityNot monotonic
2022-08-09T11:53:12.323938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.27
 
1.3%
4.57
 
1.3%
6.65
 
0.9%
4.15
 
0.9%
7.25
 
0.9%
6.94
 
0.7%
8.094
 
0.7%
8.734
 
0.7%
5.74
 
0.7%
12.094
 
0.7%
Other values (375)503
91.1%
ValueCountFrequency (%)
3.63
0.5%
3.73
0.5%
3.82
 
0.4%
3.94
0.7%
43
0.5%
4.15
0.9%
4.27
1.3%
4.34
0.7%
4.42
 
0.4%
4.57
1.3%
ValueCountFrequency (%)
14.191
0.2%
14.111
0.2%
14.091
0.2%
13.951
0.2%
13.891
0.2%
13.871
0.2%
13.851
0.2%
13.761
0.2%
13.751
0.2%
13.661
0.2%

Average Retail Price of Electricity, Commercial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Price paid by commercial entities

Distinct341
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.145330862
Minimum3.6
Maximum11.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:12.475911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile4.3
Q17.15
median7.715
Q310.0225
95-th percentile10.9345
Maximum11.7
Range8.1
Interquartile range (IQR)2.8725

Descriptive statistics

Standard deviation1.92207887
Coefficient of variation (CV)0.2359730872
Kurtosis-0.4836040987
Mean8.145330862
Median Absolute Deviation (MAD)0.915
Skewness-0.2511391454
Sum4496.222636
Variance3.694387181
MonotonicityNot monotonic
2022-08-09T11:53:12.622943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.48
 
1.4%
6.58
 
1.4%
4.17
 
1.3%
6.97
 
1.3%
9.966
 
1.1%
5.75
 
0.9%
10.465
 
0.9%
4.35
 
0.9%
7.15
 
0.9%
7.495
 
0.9%
Other values (331)491
88.9%
ValueCountFrequency (%)
3.61
 
0.2%
3.73
 
0.5%
3.84
0.7%
3.92
 
0.4%
44
0.7%
4.17
1.3%
4.23
 
0.5%
4.35
0.9%
4.48
1.4%
4.53
 
0.5%
ValueCountFrequency (%)
11.71
0.2%
11.562
0.4%
11.512
0.4%
11.342
0.4%
11.21
0.2%
11.172
0.4%
11.161
0.2%
11.11
0.2%
11.072
0.4%
11.051
0.2%

Average Retail Price of Electricity, Industrial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Price paid by industrial companies

Distinct294
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.296193073
Minimum2.2
Maximum7.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:12.783947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.8
Q14.653200039
median4.982856864
Q36.54
95-th percentile7.23
Maximum7.9
Range5.7
Interquartile range (IQR)1.886799961

Descriptive statistics

Standard deviation1.297440327
Coefficient of variation (CV)0.2449760251
Kurtosis-0.4303019558
Mean5.296193073
Median Absolute Deviation (MAD)0.665
Skewness-0.2074464765
Sum2923.498576
Variance1.683351403
MonotonicityNot monotonic
2022-08-09T11:53:12.928931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510
 
1.8%
2.810
 
1.8%
2.48
 
1.4%
4.738
 
1.4%
3.96
 
1.1%
2.66
 
1.1%
6.536
 
1.1%
4.85
 
0.9%
4.785
 
0.9%
5.045
 
0.9%
Other values (284)483
87.5%
ValueCountFrequency (%)
2.24
 
0.7%
2.32
 
0.4%
2.48
1.4%
2.55
0.9%
2.66
1.1%
2.71
 
0.2%
2.810
1.8%
2.94
 
0.7%
32
 
0.4%
3.12
 
0.4%
ValueCountFrequency (%)
7.91
0.2%
7.721
0.2%
7.691
0.2%
7.641
0.2%
7.621
0.2%
7.551
0.2%
7.531
0.2%
7.521
0.2%
7.511
0.2%
7.461
0.2%

Average Retail Price of Electricity, Transportation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Price paid by electrical transportation users

Distinct149
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.450597826
Minimum6.62
Maximum11.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:13.228946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.62
5-th percentile7.23
Q17.42
median7.75
Q39.76
95-th percentile10.689
Maximum11.84
Range5.22
Interquartile range (IQR)2.34

Descriptive statistics

Standard deviation1.285942212
Coefficient of variation (CV)0.1521717444
Kurtosis-1.006647322
Mean8.450597826
Median Absolute Deviation (MAD)0.49
Skewness0.7412437217
Sum4664.73
Variance1.653647373
MonotonicityNot monotonic
2022-08-09T11:53:13.379913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.3229
 
5.3%
8.0129
 
5.3%
7.8429
 
5.3%
7.4229
 
5.3%
7.2328
 
5.1%
7.6128
 
5.1%
7.5728
 
5.1%
7.7528
 
5.1%
7.6528
 
5.1%
7.6328
 
5.1%
Other values (139)268
48.6%
ValueCountFrequency (%)
6.621
 
0.2%
6.661
 
0.2%
6.71
 
0.2%
7.121
 
0.2%
7.131
 
0.2%
7.151
 
0.2%
7.161
 
0.2%
7.2328
5.1%
7.2428
5.1%
7.2628
5.1%
ValueCountFrequency (%)
11.841
0.2%
11.831
0.2%
11.81
0.2%
11.651
0.2%
11.211
0.2%
11.171
0.2%
11.161
0.2%
11.111
0.2%
11.041
0.2%
11.021
0.2%

Average Retail Price of Electricity, Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Cumulative mean of all categories

Distinct329
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.583192671
Minimum3
Maximum11.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2022-08-09T11:53:13.521944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.7
Q16.4
median7.02
Q39.6625
95-th percentile10.799
Maximum11.66
Range8.66
Interquartile range (IQR)3.2625

Descriptive statistics

Standard deviation2.11579835
Coefficient of variation (CV)0.2790115512
Kurtosis-0.7379649341
Mean7.583192671
Median Absolute Deviation (MAD)1.02
Skewness-0.04501739978
Sum4185.922354
Variance4.47660266
MonotonicityNot monotonic
2022-08-09T11:53:13.666932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.17
 
1.3%
6.47
 
1.3%
3.77
 
1.3%
4.26
 
1.1%
6.656
 
1.1%
3.36
 
1.1%
3.86
 
1.1%
10.285
 
0.9%
55
 
0.9%
9.645
 
0.9%
Other values (319)492
89.1%
ValueCountFrequency (%)
32
 
0.4%
3.13
0.5%
3.23
0.5%
3.36
1.1%
3.45
0.9%
3.54
0.7%
3.64
0.7%
3.77
1.3%
3.86
1.1%
3.91
 
0.2%
ValueCountFrequency (%)
11.661
0.2%
11.631
0.2%
11.541
0.2%
11.41
0.2%
11.311
0.2%
11.31
0.2%
11.211
0.2%
11.11
0.2%
11.061
0.2%
11.051
0.2%

Interactions

2022-08-09T11:53:09.727966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:02.592148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:04.457958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.415328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:06.564302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.587292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:08.634291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:10.177044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:02.962114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:04.711050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.667424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:06.826309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.832294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:08.908324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:10.287529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:03.201116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:04.824064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.787471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:06.949278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.949290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:09.032292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:10.406577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:03.495114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:04.935062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.904511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.071295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:08.073326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:09.161318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:10.540622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:03.764113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.063256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:06.196278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.206290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:08.213291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:09.296291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:10.666621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:03.981072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.189313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:06.321278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.335323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:08.348292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:09.421288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:10.793620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:04.204089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:05.298357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:06.445303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:07.460289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:08.493325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:09.562942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-09T11:53:13.783943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-09T11:53:13.989912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-09T11:53:14.230908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-09T11:53:14.475910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-09T11:53:10.969711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-09T11:53:11.188753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateYearMonthAverage Retail Price of Electricity, ResidentialAverage Retail Price of Electricity, CommercialAverage Retail Price of Electricity, IndustrialAverage Retail Price of Electricity, TransportationAverage Retail Price of Electricity, Total
01976-01-01197613.63.82.47.233.2
11976-02-01197623.73.92.47.423.3
21976-03-01197634.04.02.47.323.3
31976-04-01197644.14.02.47.613.3
41976-05-01197654.24.12.57.573.4
51976-06-01197664.24.12.57.753.4
61976-07-01197673.93.72.38.013.0
71976-08-01197683.73.62.27.843.0
81976-09-01197693.83.72.27.653.1
91976-10-011976103.93.82.27.633.2

Last rows

DateYearMonthAverage Retail Price of Electricity, ResidentialAverage Retail Price of Electricity, CommercialAverage Retail Price of Electricity, IndustrialAverage Retail Price of Electricity, TransportationAverage Retail Price of Electricity, Total
5422021-03-012021313.3011.177.059.7910.93
5432021-04-012021413.7610.936.7610.1110.70
5442021-05-012021513.8910.906.7110.0710.75
5452021-06-012021613.8511.347.2810.3211.30
5462021-07-012021713.8711.517.5210.1811.54
5472021-08-012021813.9511.567.6410.1711.63
5482021-09-012021914.1911.707.6911.1611.66
5492021-10-0120211014.0911.567.5310.2711.31
5502021-11-0120211114.1111.347.4610.4811.21
5512021-12-0120211213.7511.207.1610.5011.10